Abstract: The present disclosure relates to a system, a method, and a product for using deep learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory storing instructions executable to construct a deep-learning network to quantify trust scores; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a trust score for each voice sample in a plurality of audio samples, generate a predicated trust score by the deep-learning network based on each voice sample in the plurality of audio samples, wherein the deep-learning network comprises a plurality of branches and an aggregation network configured to aggregate results from the plurality of branches, and train the deep-learning network based on the predicated trust score and the trust score for each voice sample to obtain a training result.

Abstract: Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

Abstract: The present disclosure relates to a system, a method, and a product for using machine learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a set of vocal features and a set of text features for each sample in audio samples; obtain a trust score for each sample; perform a preprocess to obtain a set of input features for each sample; determine a type of machine-learning algorithm for the machine-learning network; tune a set of hyper parameters for the machine-learning network; generate a predicated trust score by the machine-learning network with the sets of input features for each sample; and train the machine-learning network based on the predicated trust score and the trust score for each sample to obtain the training result.

Abstract: Implementations are directed to receiving a product profile comprising an image of a product and a text description of the product; encoding the image and the text description of the product to obtain an image vector and a textual vector in a latent space; wherein the encoding comprises encoding the image and the text description using one or more encoders, each encoder corresponding to a respective data type; concatenating the image vector and the textual vector to provide a total latent vector; processing the total latent vector through a neural recommendation model to generate a score for each feature included in a plurality of features, wherein the score for a feature indicates a likelihood of the feature being included as a feature of the product for product development; and generating a recommendation comprising a set of candidate features for the product based on the score of each feature.

Abstract: A system and method for lead conversion using conversational virtual avatar is disclosed. System comprising processor causes Conversation Virtual Avatar Platform (CVAP) to receive, for first entity, from lead prioritization engine, leads applicable to first entity via lead repository based on scores associated with respective leads. Processor causes CVAP to receive, through conversation management engine (CME) configured in CVAP, from leads, responses to questions pertaining to product attributes and information pertaining to lead.

Abstract: Implementations of the present disclosure include receiving a goal, providing a problem-specific knowledge graph that is responsive to at least a portion of the goal, determining a set of events from the problem-specific knowledge graph, processing data representative of events in the set of events through a first machine learning (ML) model to provide a set of event scores, each event score in the set of event scores being associated with a respective event in the set of events, determining a sub-set of events based on the set of event scores, for each event in the sub-set of events, determining at least one action by processing a sequence of actions through a second ML model, and outputting the sub-set of events and a set of actions for execution of at least one action in the set of actions.

Abstract: Implementations are directed to receiving a set of tuples, each tuple including an entity and a product from a set of products, for each tuple: generating, by an embedding module, a total latent vector as input to a recommender network, the total latent vector generated based on a structural vector, a textual vector, and a categorical vector, each generated based on a product profile of a respective product and an entity profile of the entity, generating, by a context integration module, a latent context vector based on a context vector representative of a context of the entity, and inputting the total latent vector and the latent context vector to the recommender network, the recommender network being trained by few-shot learning using a multi-task loss function, and generating, by the recommender network, a prediction including a set of recommendations specific to the entity.

Abstract: Techniques are provided for automating allocation of resources based on business decisions. An impact of a business decision is quantified in terms of information technology (IT) metrics. The resources that may be needed to address the impact are estimated. The estimated resources are provisioned.

Abstract: Techniques are provided for allocating resources. Performance metrics for a transaction are received. It is determined whether one or more service level objectives are being violated based on the received performance metrics. In response to determining that the one or more service level objectives are being violated, additional resources are allocated to the transaction. In response to allocating the additional resources, a resource allocation event is published.

Abstract: An apparatus, system, and method are disclosed for provisioning database resource within a grid database system. The apparatus comprises an analysis module and a provision module. The analysis module analyzes a data query stream from an application to a database instance and determines that the data query stream exhibits a predetermined performance attribute. The provision module provisions a database resource in response to a determination that the data query stream exhibits the predetermined performance attribute. The provisioned database resource may be a database instance, a database server, or a cache. The provisioning of the new database resource advantageously is substantially transparent to a client on the database system.

Abstract: The techniques in the present invention allow both text and handwritten queries, and the queries can be single-word or multiword. Generally, each handwritten word in a handwritten document is converted to a document stack of words, where each document stack contains a list of text words and a word score of some type for each text word in the list. The query is also converted to one or more stacks of words. A measure is determined from each query and document stack. Documents that meet search criteria in the query are then selected based on the query and the values of the measures. The present invention also performs multiple recognitions, with multiple recognizers, on a handwritten document to create multiple recognized transcriptions of the document. The multiple transcriptions are used for document retrieval. In another embodiment, a single transcription is created from the multiple transcriptions, and the single transcription is used for document retrieval.

Abstract: In one aspect of the present invention, page breaks are identified in the following manner. A set of ink data and a document description are processed by a variety of scoring methods, each of which generates a score for each possible insertion point in the ink. These scores are combined to produce a ranked list of hypothesized page breaks for the corresponding ink data. This ranked list is then used either to insert page breaks automatically using a predefined threshold to determine a cut-off in the list; or to present, on-line, to a human for verification/approval; or a mixture of the two based on two thresholds: one for automatic insertion and the other for human verification. It is to be understood not all scoring methods need be used, that is, one or more of the scoring methods may be used as needed.

Abstract: Techniques are provided for automating allocation of resources based on business decisions. An impact of a business decision is quantified in terms of information technology (IT) metrics. The resources that may be needed to address the impact are estimated. The estimated resources are provisioned.

Abstract: Techniques are provided for allocating resources. Performance metrics for a transaction are received. It is determined whether one or more service level objectives are being violated based on the received performance metrics. In response to determining that the one or more service level objectives are being violated, additional resources are allocated to the transaction. In response to allocating the additional resources, a resource allocation event is published.

Abstract: An apparatus, system, and method are disclosed for provisioning database resource within a grid database system. The federation apparatus includes an analysis module and a provision module. The analysis module analyzes a data query stream from an application to a database instance and determines if the data query stream exhibits a predetermined performance attribute. The provision module provisions a database resource in response to a determination that the data query stream exhibits the predetermined performance attribute. The provisioned database resource may be a database instance or a cache. The provisioning of the new database resource advantageously is substantially transparent to a client on the database system.

Abstract: In one aspect of the present invention, page breaks are identified in the following manner. A set of ink data and a document description are processed by a variety of scoring methods, each of which generates a score for each possible insertion point in the ink. These scores are combined to produce a ranked list of hypothesized page breaks for the corresponding ink data. This ranked list is then used either to insert page breaks automatically using a predefined threshold to determine a cut-off in the list; or to present, on-line, to a human for verification/approval; or a mixture of the two based on two thresholds: one for automatic insertion and the other for human verification. It is to be understood not all scoring methods need be used, that is, one or more of the scoring methods may be used as needed.

Abstract: Several methods, and related apparatus, are provided for the entry of formatted ink data (i.e., electronic ink) such that individual items in the data may be parsed and recognized more effectively. Each method allows users to enter formatted ink data in-line, which can then be recognized with constraints and parsed for use in other application programs or databases. In addition, a method is provided for allowing user-specialization of any of these entry methods (or other similar) methods. Note that in any of these methods, the user may send the formatted ink either to the default ink-processing application, or else directly to another application or database.

Abstract: A system and method for automatically providing quality assurance for user enrollment in a recognition system. Advantageously, the quality a new enrollment (i.e., a newly trained user-dependent prototype) is assessed before the new enrollment is accepted in place of a current enrollment. This quality check is performed by decoding stored user test data using the new enrollment, comparing the decoding results of the new enrollment to the known script used to generate the test data to obtain an accuracy score for the new enrollment, and then comparing the accuracy score for the new enrollment with an accuracy score of a previous qualified enrollment (or, in the case where there is no previous, qualified enrollment, to the accuracy of the speaker independent model). If the decoding results of the new enrollment are acceptable, the new enrollment will be used for recognition; otherwise it will be rejected and discarded.

Abstract: Methods and apparatus for the processing, archival and retrieval of multiple data streams that are captured by multiple input modalities. A processing method preferably transforms the captured data into a form that is acceptable for the compatible archival method.

Abstract: The techniques in the present invention allow both text and handwritten queries, and the queries can be single-word or multiword. Generally, each handwritten word in a handwritten document is converted to a document stack of words, where each document stack contains a list of text words and a word score of some type for each text word in the list. The query is also converted to one or more stacks of words. A measure is determined from each query and document stack. Documents that meet search criteria in the query are then selected based on the query and the values of the measures. The present invention also performs multiple recognitions, with multiple recognizers, on a handwritten document to create multiple recognized transcriptions of the document. The multiple transcriptions are used for document retrieval. In another embodiment, a single transcription is created from the multiple transcriptions, and the single transcription is used for document retrieval.